AI Publications

1. AI will boost productivity, but not lead to an immediate boom in growth across the entire economy.

AI is undoubtedly a powerful driver of productivity, especially in industries where automation, data processing, and predictive modeling can yield direct efficiency gains. However, the overall economy is far too complex for these gains to translate into an immediate, economy-wide boom. Growth requires not only technological breakthroughs but also complementary changes in management, infrastructure, and culture.

Many sectors—such as healthcare, education, and government services—remain resistant to rapid transformation due to regulation, legacy systems, and human-intensive processes. While AI can revolutionize tasks like coding, logistics, and content creation, its diffusion across slower-moving institutions will take time. The economic uplift will therefore unfold unevenly, appearing first in scalable industries and only gradually affecting the broader economic landscape.

2. The key bottlenecks to AI-enabled growth are human, institutional, and organisational — not just compute or algorithmic advances.

Technological progress often outpaces human and institutional readiness. The greatest limits to AI adoption are less about computing power or model architecture, and more about people’s ability to adapt to new workflows, reimagine roles, and trust machine-assisted decisions. Organisations must reorganize hierarchies, retrain workers, and redesign incentives to fully benefit from AI.

Institutional inertia—rigid regulatory systems, slow corporate governance, and misaligned incentives—can easily offset the efficiency AI promises. True economic transformation requires rethinking processes and decision structures at every level, ensuring that AI complements human judgment instead of replacing it without coordination.

In other words, growth is not just a matter of smarter machines; it depends on smarter systems in which humans and machines work symbiotically.

3. Diffusion of new technologies is slow; what matters is how fast AI gets embedded in practice.

History shows that even the most transformative technologies—electricity, the internet, or personal computing—took decades to fully permeate economies. The challenge is not invention but adoption.

AI faces similar diffusion hurdles: high implementation costs, uncertainty about return on investment, ethical concerns, and skill gaps in the workforce. For AI to reshape productivity broadly, it must be embedded in everyday routines and standard operating procedures across firms. This embedding process demands new training programs, technical infrastructure, and cultural shifts in how decisions are made.

The speed of diffusion will vary by country and sector, depending on how willing organisations are to experiment and absorb risk. The true revolution, therefore, lies not in breakthroughs at research labs but in how quickly and effectively ordinary firms adopt and integrate these systems.

4. Some sectors will benefit far more from AI than others; sectoral heterogeneity is critical.

AI's impact will be uneven across the economy. Sectors rich in digital data and modular workflows—finance, marketing, software, logistics—can harness AI faster and more efficiently than sectors bound by human interaction or regulation. Industries like education, public administration, or healthcare often rely on interpersonal trust and compliance frameworks that limit full automation.

This creates a two-speed economy: one set of sectors scaling rapidly through AI, and another moving at a slower pace. The sectors that manage to combine automation with innovation will set new productivity frontiers, while others risk lagging behind. The result is not uniform transformation but a patchwork of progress that reshapes competition and redistributes economic influence within and across industries.

5. AI raises the marginal value of human inputs outside of pure intelligence — e.g., creativity, coordination, human judgment.

As machines take over tasks once requiring cognitive processing, the comparative advantage of humans shifts. Qualities such as creativity, emotional intelligence, moral reasoning, and the ability to coordinate diverse teams become more valuable. In environments where information is abundant, what matters most is not generating content but curating, contextualizing, and interpreting it.

Humans who can frame questions, evaluate trade-offs, and make nuanced judgments will become essential. The rise of AI paradoxically makes uniquely human traits more important. Those who combine technical literacy with deep human understanding will define the next era of leadership and innovation.

6. Variance among human outcomes will increase — the winners will win more and the middle may struggle.

AI amplifies inequality within labour markets by rewarding exceptional talent, entrepreneurial risk-taking, and technological leverage. Individuals or firms that know how to deploy AI effectively can scale output exponentially, while others see little or no productivity improvement. This “superstar effect” widens the gap between the top performers and the rest, potentially hollowing out the middle class.

Moreover, those without access to advanced tools, education, or networks risk marginalisation as AI reshapes employment patterns. Economic policy will need to grapple with this divergence—ensuring that opportunity, not just efficiency, remains central to growth.

7. Education and skills systems must adjust: knowing how to work with AI becomes more important than conventional instruction.

Traditional education systems often emphasise memorisation and standardised knowledge, but AI changes what knowledge is useful. The future workforce will need to master collaboration with intelligent systems, critical thinking, and adaptability. Learning must become continuous, integrating AI tools directly into the educational process so that students grow comfortable using them as extensions of reasoning.

Curricula will have to evolve beyond teaching information to teaching interpretation, synthesis, and problem formulation. The ultimate educational challenge is to cultivate a population that can coexist and co-create with machines rather than compete against them.

8. The geopolitical and economic power implications of AI are substantial: countries with deep capital markets, strong institutions, and entrepreneurial culture may capture more of the gains.

AI’s transformative capacity magnifies existing structural advantages between nations. Economies that combine abundant capital, flexible institutions, and dynamic private sectors will harness AI faster and dominate global innovation networks. Conversely, countries with weak governance, poor infrastructure, or rigid regulation may struggle to attract investment and talent.

This divergence could redefine global power hierarchies, concentrating technological and financial influence in a few leading hubs. AI is thus not merely an economic tool but a strategic asset—one that reshapes trade, diplomacy, and security. Nations capable of integrating AI into governance, defense, and industry will set the pace of global progress, while others risk technological dependency.